This Project focuses on (1) The development of methodology for incorporating "cutting-edge" research findings into future risk assessments; (2) The development of methods for designing studies to improve risk estimates, especially when mechanistic data is involved; (3) The development of methods for the evaluation of exposure, dose-response shape and potency; (4) The development of methods for evaluating mixtures when multiple mechanisms are involved; (5) The development of methods which harmonize cancer and non-cancer health risk assessments; (6) Direct engagement of the regulatory community through expert panels, peer review and collaborative research; (7) Practical improvement of stochastic processes through careful linkage of theoretical developments with computational methods that are accurate and convenient; (8) Collaboration with research groups within the NIEHS and research groups doing similar work to improve the biological understanding of disease incidence; (9) Iterative improvement of the biological basis for disease incidence models through a process of hypothesis testing and laboratory research; (10) Linkage of disease incidence models to toxicokinetics models in a scientifically credible manner; (11) Use of the broadest array of data in both the development of the model and its application; (12) Support of the National Toxicology Program. We developed a quantitative, statistically sound methodology for the analysis of suspected gene regulatory networks using gene expression data sets. The method is based on Bayesian networks and provides a means to directly quantify gene-expression networks and test hypotheses regarding the linkages between genes in this network. Simulation studies were performed to evaluate the behavior of this method for small samples and to address the design of future studies aimed at quantifying gene-interaction networks. Using gene expression changes in HPL1A lung airway epithelial cells after exposure to TCDD at levels of 0.1, 1.0 and 10.0 nM for 24 hours, a hypothesized gene expression network was analyzed. The method supports the assumed network and allowed the evaluation of a hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system (see Research Theme 2.A below). One of the major unresolved issues in the analysis of gene expression data is the identification of gene regulatory networks. Several methods have been proposed by others for identifying gene regulatory networks, but these methods focus on the use of multiple pairwise comparisons to identify the network structure. We developed a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods, this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the data. Bayesian methods (as above) can then be used to quantify the linkages between genes to provide a complete characterization of the resulting gene-expression network. Simulation studies were performed to evaluate the operating characteristics of the method and to determine the probabilities of finding the correct network under different design strategies. This method was applied to data on G(1)/S activation in mouse fetal fibrosis MF129 cells. The resulting gene-interaction network was used to identify the nodal genes and quantify the relationships among genes within the network. Searches for common transcription factors were used to validate the resulting networks. We have also developed computer software to enable researchers to use our Bayesian networks analysis method to analyze gene expression data. A user-friendly, windows-based format was used to make it easy for researchers to choose options for the analysis, define network structures and evaluate the resulting analysis.